Data validation, false vectors correction and derived magnitudes calculation on PIV data

Nogueira, J.; Lecuona, A.; Rodríguez, Pedro

doi:10.1088/0957-0233/8/12/012

Cited by 118 publications

(73 citation statements)

References 6 publications

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“…Concerning image postprocessing, attention has been given to erroneous vector (outlier) detection and replacement. The outlier detection scheme used, based on a twelve point algorithm, combined with an horizontal and vertical spreading procedure all over the field (at least four points for validation) and with the iterative outlier replacement scheme based on a 25 point D-filter (Nogueira et al 1997) gives the best solution and is suitable to many different image conditions.…”

Section: Urs-1/ciramentioning

confidence: 99%

Main results of the third international PIV Challenge

Stanislas¹,

et al. 2008

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This paper presents the main results of the third international PIV Challenge which took place in Pasadena (USA) on the 19th and 20th of September 2005. This workshop was linked to the PIV05 International Symposium held at the same place the same week. The present contribution states the objectives of the challenge, describes the test cases and the algorithms used by the participants, and presents the main results together with some discussion and conclusions on the accuracy and robustness of various PIV and PTV algorithms. As the entire amount of results obtained cannot be detailed, this contribution is written as a guide for the use of the full database of images and results which is available at http://www.pivChallenge.org.

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Section: Urs-1/ciramentioning

confidence: 99%

Main results of the third international PIV Challenge

Stanislas¹,

et al. 2008

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“…The flow field was initially assumed to have the nominal through-plane flow velocity. After processing, vectors were eliminated if the signal-to-noise ratio was less than 2, and by an iterative procedure that used the median and RMS velocities in a 3ϫ3 vector neighborhood (similar to Nogueira et al, 1997). Finally, a 3ϫ3 spatial smoothing filter was applied, followed by a 25·Hz low-pass finite impulse response temporal filter.…”

Section: Swimming Protocol and Flow Visualizationmentioning

confidence: 99%

Median fin function in bluegill sunfishLepomis macrochirus:streamwise vortex structure during steady swimming

Tytell

2006

Journal of Experimental Biology

116

107

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SUMMARY Fishes have an enormous diversity of body shapes and fin morphologies. From a hydrodynamic standpoint, the functional significance of this diversity is poorly understood, largely because the three-dimensional flow around swimming fish is almost completely unknown. Fully three-dimensional volumetric flow measurements are not currently feasible, but measurements in multiple transverse planes along the body can illuminate many of the important flow features. In this study, I analyze flow in the transverse plane at a range of positions around bluegill sunfish Lepomis macrochirus, from the trailing edges of the dorsal and anal fins to the near wake. Simultaneous particle image velocimetry and kinematic measurements were performed during swimming at 1.2 body lengths s–1 to describe the streamwise vortex structure, to quantify the contributions of each fin to the vortex wake, and to assess the importance of three-dimensional flow effects in swimming. Sunfish produce streamwise vortices from at least eight distinct places,including both the dorsal and ventral margins of the soft dorsal and anal fins, and the tips and central notched region of the caudal fin. I propose a three-dimensional structure of the vortex wake in which these vortices from the caudal notch are elongated by the dorso-ventral cupping motion of the tail, producing a structure like a hairpin vortex in the caudal fin vortex ring. Vortices from the dorsal and anal fin persist into the wake, probably linking up with the caudal fin vortices. These dorsal and anal fin vortices do not differ significantly in circulation from the two caudal fin tip vortices. Because the circulations are equal and the length of the trailing edge of the caudal fin is approximately equal to the combined trailing edge length of the dorsal and anal fins, I argue that the two anterior median fins produce a total force that is comparable to that of the caudal fin. To provide additional detail on how different positions contribute to total force along the posterior body, the change in vortex circulation as flow passes down the body is also analyzed. The posterior half of the caudal fin and the dorsal and anal fins add vortex circulation to the flow, but circulation appears to decrease around the peduncle and anterior caudal fin. Kinematic measurements indicate that the tail is angled correctly to enhance thrust through this interaction. Finally, the degree to which the caudal fin acts like a idealized two-dimensional plate is examined: approximately 25% of the flow near the tail is accelerated up and down, rather than laterally, producing wasted momentum,a loss not present in ideal two-dimensional theories.

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“…Normally, the selection of such kind of parameters in PIV is done on hit and trial basis till the maximum numbers of true vectors are recovered. In the standard images tested here it is easy to verify the true vectors as their correct answer is known while in the case of real-world images it should be done with the aid of some validation algorithms [30], [31].…”

Section: Resultsmentioning

confidence: 99%

“…Hence is such case of overlapping and missing particles the ambiguous situation may arise producing the outliers. Such kind of outliers should be removed using proper outlier dection algorithms [30], [31], [35]. Similarly, it should also be noted that some optical conditions of recording and computation of the image centroid from such images may also affect the final pairing results.…”

Section: Resultsmentioning

confidence: 99%

A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements

Sapkota

Ohmi

2009

IEICE Trans. Inf. & Syst.

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SUMMARYParticle Image Velocimetry (PIV) is a widely used tool for the measurement of the different kinematic properties of the fluid flow. In this measurement technique, a pulsed laser light sheet is used to illuminate a flow field seeded with tracer particles and at each instance of illumination, the positions of the particles are recorded on digital CCD cameras. The resulting two camera frames can then be processed by various techniques to obtain the velocity vectors. One such techniques involve the tracking of the individual particles so as to identify the displacement of the every particles present in the flow field. The displacement of individual particles thus determined gives the velocity information if divided by known time interval. The accuracy as well as efficiency of such measurement systems depend upon the reliability of the algorithms to track those particles. In the present work, a cellular neural network based algorithm has been proposed. Performance test has been carried out using the standard flow images. It performs well in comparison to the existing algorithms in terms of reliability, accuracy and processing time. key words: flow measurement, particle image velocimetry, particle tracking velocimery, neural network, optimization

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Data validation, false vectors correction and derived magnitudes calculation on PIV data

Cited by 118 publications

References 6 publications

Main results of the third international PIV Challenge

Main results of the third international PIV Challenge

Median fin function in bluegill sunfishLepomis macrochirus:streamwise vortex structure during steady swimming

A Neural Network Based Algorithm for Particle Pairing Problem of PIV Measurements

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